Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations582
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory2.4 KiB

Variable types

Text6
Categorical12
Numeric6
Boolean2
Unsupported1
URL1

Alerts

favorite has constant value "False" Constant
is_search has constant value "True" Constant
creative_features_creative_concept is highly overall correlated with creative_features_creative_themeHigh correlation
creative_features_creative_theme is highly overall correlated with creative_features_creative_conceptHigh correlation
ctr is highly overall correlated with scrap_datetimeHigh correlation
industry_parent.value is highly overall correlated with scrap_datetimeHigh correlation
objective_id is highly overall correlated with objective_value and 1 other fieldsHigh correlation
objective_value is highly overall correlated with objective_id and 1 other fieldsHigh correlation
scrap_datetime is highly overall correlated with ctr and 3 other fieldsHigh correlation
video_info_height is highly overall correlated with video_info_widthHigh correlation
video_info_width is highly overall correlated with video_info_heightHigh correlation
creative_features_format_production_style is highly imbalanced (78.4%) Imbalance
like is highly skewed (γ1 = 24.12027672) Skewed
video_name has unique values Unique
video_info_cover has unique values Unique
tag is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-04-12 19:56:11.069525
Analysis finished2025-04-12 19:56:33.958327
Duration22.89 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct496
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size126.2 KiB
2025-04-12T16:56:34.106884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length972
Median length195
Mean length66.228522
Min length0

Characters and Unicode

Total characters38545
Distinct characters198
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique439 ?
Unique (%)75.4%

Sample

1st rowGreat time killer!
2nd rowOddly satisfying game
3rd rowMy friend recommended me to play this game
4th rowdownload now
5th rowPlay ten minutes a day to relieve stress!
ValueCountFrequency (%)
the 146
 
2.4%
to 141
 
2.3%
and 99
 
1.6%
your 99
 
1.6%
85
 
1.4%
a 82
 
1.4%
for 81
 
1.3%
you 73
 
1.2%
get 67
 
1.1%
is 54
 
0.9%
Other values (2306) 5126
84.7%
2025-04-12T16:56:34.368835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5589
 
14.5%
e 3245
 
8.4%
o 2275
 
5.9%
a 2238
 
5.8%
t 2215
 
5.7%
i 2017
 
5.2%
r 1742
 
4.5%
s 1723
 
4.5%
n 1684
 
4.4%
l 1333
 
3.5%
Other values (188) 14484
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 38545
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5589
 
14.5%
e 3245
 
8.4%
o 2275
 
5.9%
a 2238
 
5.8%
t 2215
 
5.7%
i 2017
 
5.2%
r 1742
 
4.5%
s 1723
 
4.5%
n 1684
 
4.4%
l 1333
 
3.5%
Other values (188) 14484
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 38545
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5589
 
14.5%
e 3245
 
8.4%
o 2275
 
5.9%
a 2238
 
5.8%
t 2215
 
5.7%
i 2017
 
5.2%
r 1742
 
4.5%
s 1723
 
4.5%
n 1684
 
4.4%
l 1333
 
3.5%
Other values (188) 14484
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 38545
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5589
 
14.5%
e 3245
 
8.4%
o 2275
 
5.9%
a 2238
 
5.8%
t 2215
 
5.7%
i 2017
 
5.2%
r 1742
 
4.5%
s 1723
 
4.5%
n 1684
 
4.4%
l 1333
 
3.5%
Other values (188) 14484
37.6%
Distinct336
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Memory size40.4 KiB
2025-04-12T16:56:34.505966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length29
Mean length12.195876
Min length0

Characters and Unicode

Total characters7098
Distinct characters93
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique275 ?
Unique (%)47.3%

Sample

1st rowSurvival Game Master
2nd rowGameworld Master
3rd rowSugarcane Inc. Empire Tycoon
4th rowSmart VPN - Fast, Stable
5th rowSugarcane Factory 3D
ValueCountFrequency (%)
57
 
5.5%
celebs 12
 
1.2%
news 12
 
1.2%
the 11
 
1.1%
bend 9
 
0.9%
stretching 9
 
0.9%
flexibility 9
 
0.9%
fabletics 9
 
0.9%
fashion 8
 
0.8%
chairs 8
 
0.8%
Other values (552) 895
86.1%
2025-04-12T16:56:34.733153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 688
 
9.7%
598
 
8.4%
a 474
 
6.7%
i 434
 
6.1%
o 381
 
5.4%
s 379
 
5.3%
r 378
 
5.3%
t 342
 
4.8%
n 325
 
4.6%
l 292
 
4.1%
Other values (83) 2807
39.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7098
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 688
 
9.7%
598
 
8.4%
a 474
 
6.7%
i 434
 
6.1%
o 381
 
5.4%
s 379
 
5.3%
r 378
 
5.3%
t 342
 
4.8%
n 325
 
4.6%
l 292
 
4.1%
Other values (83) 2807
39.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7098
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 688
 
9.7%
598
 
8.4%
a 474
 
6.7%
i 434
 
6.1%
o 381
 
5.4%
s 379
 
5.3%
r 378
 
5.3%
t 342
 
4.8%
n 325
 
4.6%
l 292
 
4.1%
Other values (83) 2807
39.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7098
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 688
 
9.7%
598
 
8.4%
a 474
 
6.7%
i 434
 
6.1%
o 381
 
5.4%
s 379
 
5.3%
r 378
 
5.3%
t 342
 
4.8%
n 325
 
4.6%
l 292
 
4.1%
Other values (83) 2807
39.5%

cost
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size33.1 KiB
2
441 
1
136 
0
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters582
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
2 441
75.8%
1 136
 
23.4%
0 5
 
0.9%

Length

2025-04-12T16:56:34.807369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T16:56:34.854962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 441
75.8%
1 136
 
23.4%
0 5
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 441
75.8%
1 136
 
23.4%
0 5
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 582
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 441
75.8%
1 136
 
23.4%
0 5
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 582
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 441
75.8%
1 136
 
23.4%
0 5
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 582
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 441
75.8%
1 136
 
23.4%
0 5
 
0.9%

ctr
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.034587629
Minimum0.01
Maximum0.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2025-04-12T16:56:34.910565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.01
median0.01
Q30.02
95-th percentile0.1595
Maximum0.94
Range0.93
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.07797845
Coefficient of variation (CV)2.2545185
Kurtosis61.282814
Mean0.034587629
Median Absolute Deviation (MAD)0
Skewness6.7454635
Sum20.13
Variance0.0060806386
MonotonicityNot monotonic
2025-04-12T16:56:34.977400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.01 397
68.2%
0.02 61
 
10.5%
0.03 28
 
4.8%
0.06 11
 
1.9%
0.04 9
 
1.5%
0.08 8
 
1.4%
0.05 7
 
1.2%
0.1 7
 
1.2%
0.07 6
 
1.0%
0.2 6
 
1.0%
Other values (22) 42
 
7.2%
ValueCountFrequency (%)
0.01 397
68.2%
0.02 61
 
10.5%
0.03 28
 
4.8%
0.04 9
 
1.5%
0.05 7
 
1.2%
0.06 11
 
1.9%
0.07 6
 
1.0%
0.08 8
 
1.4%
0.09 3
 
0.5%
0.1 7
 
1.2%
ValueCountFrequency (%)
0.94 1
 
0.2%
0.84 1
 
0.2%
0.7 1
 
0.2%
0.41 1
 
0.2%
0.38 1
 
0.2%
0.37 1
 
0.2%
0.3 1
 
0.2%
0.27 1
 
0.2%
0.26 3
0.5%
0.24 1
 
0.2%

favorite
Boolean

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size710.0 B
False
582 
ValueCountFrequency (%)
False 582
100.0%
2025-04-12T16:56:35.018564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

id
Real number (ℝ)

Distinct578
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2501063 × 1018
Minimum6.8002568 × 1018
Maximum7.4869781 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2025-04-12T16:56:35.070090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.8002568 × 1018
5-th percentile7.0710348 × 1018
Q17.1836907 × 1018
median7.2428798 × 1018
Q37.3060237 × 1018
95-th percentile7.4696379 × 1018
Maximum7.4869781 × 1018
Range6.8672131 × 1017
Interquartile range (IQR)1.2233298 × 1017

Descriptive statistics

Standard deviation1.1051749 × 1017
Coefficient of variation (CV)0.015243569
Kurtosis0.66123625
Mean7.2501063 × 1018
Median Absolute Deviation (MAD)6.1933854 × 1016
Skewness0.021221115
Sum-4.7425037 × 1018
Variance1.2214116 × 1034
MonotonicityNot monotonic
2025-04-12T16:56:35.151802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.033790587 × 10182
 
0.3%
7.061134395 × 10182
 
0.3%
7.052699606 × 10182
 
0.3%
6.930891822 × 10182
 
0.3%
7.132878852 × 10181
 
0.2%
7.417062473 × 10181
 
0.2%
7.16260997 × 10181
 
0.2%
7.46698774 × 10181
 
0.2%
7.143523146 × 10181
 
0.2%
7.319472428 × 10181
 
0.2%
Other values (568) 568
97.6%
ValueCountFrequency (%)
6.800256788 × 10181
0.2%
6.927329073 × 10181
0.2%
6.930891822 × 10182
0.3%
6.937371977 × 10181
0.2%
6.948334706 × 10181
0.2%
6.952823417 × 10181
0.2%
6.961989163 × 10181
0.2%
6.979917279 × 10181
0.2%
6.985183241 × 10181
0.2%
6.987824363 × 10181
0.2%
ValueCountFrequency (%)
7.486978098 × 10181
0.2%
7.486181966 × 10181
0.2%
7.48584039 × 10181
0.2%
7.485327558 × 10181
0.2%
7.485309949 × 10181
0.2%
7.485292941 × 10181
0.2%
7.485013273 × 10181
0.2%
7.484641495 × 10181
0.2%
7.48405999 × 10181
0.2%
7.481418077 × 10181
0.2%

is_search
Boolean

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size710.0 B
True
582 
ValueCountFrequency (%)
True 582
100.0%
2025-04-12T16:56:35.200618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

like
Real number (ℝ)

Skewed 

Distinct567
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean912594.44
Minimum1710
Maximum5.1156535 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2025-04-12T16:56:35.253319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1710
5-th percentile1839.15
Q12831.25
median6104
Q317039.75
95-th percentile109347.15
Maximum5.1156535 × 108
Range5.1156364 × 108
Interquartile range (IQR)14208.5

Descriptive statistics

Standard deviation21204963
Coefficient of variation (CV)23.235911
Kurtosis581.85729
Mean912594.44
Median Absolute Deviation (MAD)3879
Skewness24.120277
Sum5.3112996 × 108
Variance4.4965047 × 1014
MonotonicityNot monotonic
2025-04-12T16:56:35.335904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2930 3
 
0.5%
1966 2
 
0.3%
2310 2
 
0.3%
6223 2
 
0.3%
1873 2
 
0.3%
7658 2
 
0.3%
2084 2
 
0.3%
67287 2
 
0.3%
1769 2
 
0.3%
84616 2
 
0.3%
Other values (557) 561
96.4%
ValueCountFrequency (%)
1710 2
0.3%
1720 1
0.2%
1725 1
0.2%
1728 1
0.2%
1729 1
0.2%
1732 1
0.2%
1736 1
0.2%
1739 1
0.2%
1740 1
0.2%
1743 1
0.2%
ValueCountFrequency (%)
511565347 1
0.2%
5505357 1
0.2%
539054 1
0.2%
480556 1
0.2%
436777 1
0.2%
391422 1
0.2%
359895 1
0.2%
291579 1
0.2%
289737 1
0.2%
275717 1
0.2%

tag
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size29.6 KiB
Distinct355
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Memory size438.8 KiB
2025-04-12T16:56:35.508192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5174
Median length1772
Mean length671.31959
Min length0

Characters and Unicode

Total characters390708
Distinct characters175
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique350 ?
Unique (%)60.1%

Sample

1st rowThis ad is using Product Review to catch audience's attention and improve ads performance. 'All right, I got them all ready. Let's see who wins. Oh, not looking good. Oh, no one want? I guess I get to keep all the money. With more money, I can actually get more contestants. And as you get more money, you can actually unlock more mini games like the. Like the glass bridge game later on down the line. And you can become the survival game masteralright? Let's go ahead. Throw them into the game. Who's gonna win this one? Who do you guys think? It's not looking good. Looks like i'm gonna. I'm gonna keep all the money again. Haha. Click here and download survival game master today.' 1. Showcase: The advertisement demonstrates the game's excitement and unpredictability by showcasing the contestants' reactions and the outcome of the game. This can pique the target audience's interest and encourage them to download the game. 2. Highlight Selling Points: The voiceover highlights the game's features, such as unlocking more mini-games and becoming the survival game master, which can entice potential players to try the game. Additionally, the text-over displays the game's high rewards, such as $300 and $8.1K, which can further motivate the target audience to download the game.
2nd rowThis ad is using Strategy Focused to catch audience's attention and improve ads performance. 1. Attention Grabber: The ad uses a variety of attention-grabbing tactics, such as bold text-overlay and abrupt voice-over interruptions, to capture viewers' attention and draw them into the advertisement. 2. Highlight Selling Points: The voice-over mentions several selling points, including a 300/2 and 300/1 ratio, VIP, and a 200 price point, which are highlighted to differentiate the product from competitors and increase its appeal to potential buyers.
3rd row
4th rowThis ad is using Oddly Satisfying to catch audience's attention and improve ads performance. 1. Comment Reply: The ad creatively utilizes comment reply as a hook to grab the audience's attention. By incorporating a popular character from Frozen, the advertisement is able to connect with a wide audience, especially children and families. 2. Respond to Comments: The ad effectively responds to comments made by the audience, showcasing the brand's engagement and interaction with its customers. This technique can increase customer loyalty and encourage more people to interact with the brand. 3. Highlight Selling Points: The ad highlights the unique selling point of the product, which is the ability to undo paint strokes. This feature is emphasized through the use of the phrase "Undo Paint stroke", creating an impression that the product is innovative and user-friendly.
5th row
ValueCountFrequency (%)
the 4776
 
7.5%
and 2468
 
3.9%
to 2082
 
3.3%
a 1425
 
2.2%
of 1351
 
2.1%
this 1074
 
1.7%
is 972
 
1.5%
product 902
 
1.4%
ad 894
 
1.4%
you 639
 
1.0%
Other values (5190) 47444
74.1%
2025-04-12T16:56:35.783343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
62389
16.0%
e 38148
 
9.8%
t 29473
 
7.5%
i 24818
 
6.4%
a 23365
 
6.0%
o 22775
 
5.8%
n 21115
 
5.4%
s 20453
 
5.2%
r 16470
 
4.2%
h 15716
 
4.0%
Other values (165) 115986
29.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 390708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
62389
16.0%
e 38148
 
9.8%
t 29473
 
7.5%
i 24818
 
6.4%
a 23365
 
6.0%
o 22775
 
5.8%
n 21115
 
5.4%
s 20453
 
5.2%
r 16470
 
4.2%
h 15716
 
4.0%
Other values (165) 115986
29.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 390708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
62389
16.0%
e 38148
 
9.8%
t 29473
 
7.5%
i 24818
 
6.4%
a 23365
 
6.0%
o 22775
 
5.8%
n 21115
 
5.4%
s 20453
 
5.2%
r 16470
 
4.2%
h 15716
 
4.0%
Other values (165) 115986
29.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 390708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
62389
16.0%
e 38148
 
9.8%
t 29473
 
7.5%
i 24818
 
6.4%
a 23365
 
6.0%
o 22775
 
5.8%
n 21115
 
5.4%
s 20453
 
5.2%
r 16470
 
4.2%
h 15716
 
4.0%
Other values (165) 115986
29.7%

scrap_datetime
Categorical

High correlation 

Distinct47
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size47.3 KiB
2025-04-04T17:53:16.735723
 
20
2025-04-04T18:42:44.917012
 
20
2025-04-04T18:26:57.088125
 
20
2025-04-04T18:54:22.439377
 
20
2025-04-04T18:52:00.345967
 
20
Other values (42)
482 

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters15132
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.9%

Sample

1st row2025-04-04T17:53:16.735723
2nd row2025-04-04T17:53:16.735723
3rd row2025-04-04T17:53:16.735723
4th row2025-04-04T17:53:16.735723
5th row2025-04-04T17:53:16.735723

Common Values

ValueCountFrequency (%)
2025-04-04T17:53:16.735723 20
 
3.4%
2025-04-04T18:42:44.917012 20
 
3.4%
2025-04-04T18:26:57.088125 20
 
3.4%
2025-04-04T18:54:22.439377 20
 
3.4%
2025-04-04T18:52:00.345967 20
 
3.4%
2025-04-04T18:49:49.171198 20
 
3.4%
2025-04-04T18:47:36.451244 20
 
3.4%
2025-04-04T18:00:18.390358 20
 
3.4%
2025-04-04T18:02:28.571250 20
 
3.4%
2025-04-04T18:45:19.627583 20
 
3.4%
Other values (37) 382
65.6%

Length

2025-04-12T16:56:35.858885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2025-04-04t17:53:16.735723 20
 
3.4%
2025-04-04t18:38:31.254995 20
 
3.4%
2025-04-04t18:42:44.917012 20
 
3.4%
2025-04-04t18:29:21.955350 20
 
3.4%
2025-04-04t18:18:17.993111 20
 
3.4%
2025-04-04t18:16:03.151165 20
 
3.4%
2025-04-04t18:13:19.730780 20
 
3.4%
2025-04-04t18:11:11.761978 20
 
3.4%
2025-04-04t18:33:58.528742 20
 
3.4%
2025-04-04t18:36:12.909615 20
 
3.4%
Other values (37) 382
65.6%

Most occurring characters

ValueCountFrequency (%)
0 2273
15.0%
2 1808
11.9%
4 1742
11.5%
1 1509
10.0%
5 1341
8.9%
- 1164
7.7%
: 1164
7.7%
8 902
 
6.0%
3 593
 
3.9%
T 582
 
3.8%
Other values (4) 2054
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2273
15.0%
2 1808
11.9%
4 1742
11.5%
1 1509
10.0%
5 1341
8.9%
- 1164
7.7%
: 1164
7.7%
8 902
 
6.0%
3 593
 
3.9%
T 582
 
3.8%
Other values (4) 2054
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2273
15.0%
2 1808
11.9%
4 1742
11.5%
1 1509
10.0%
5 1341
8.9%
- 1164
7.7%
: 1164
7.7%
8 902
 
6.0%
3 593
 
3.9%
T 582
 
3.8%
Other values (4) 2054
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2273
15.0%
2 1808
11.9%
4 1742
11.5%
1 1509
10.0%
5 1341
8.9%
- 1164
7.7%
: 1164
7.7%
8 902
 
6.0%
3 593
 
3.9%
T 582
 
3.8%
Other values (4) 2054
13.6%

video_name
Text

Unique 

Distinct582
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size44.2 KiB
2025-04-12T16:56:35.967601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length21
Mean length20.524055
Min length20

Characters and Unicode

Total characters11945
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique582 ?
Unique (%)100.0%

Sample

1st rowad_23000000000_2_0_1
2nd rowad_23000000000_2_1_1
3rd rowad_23000000000_2_2_1
4th rowad_23000000000_2_3_1
5th rowad_23000000000_2_4_1
ValueCountFrequency (%)
ad_23000000000_2_0_1 1
 
0.2%
ad_20000000000_2_5_1 1
 
0.2%
ad_23000000000_2_17_1 1
 
0.2%
ad_23000000000_2_8_1 1
 
0.2%
ad_23000000000_2_2_1 1
 
0.2%
ad_23000000000_2_3_1 1
 
0.2%
ad_23000000000_2_4_1 1
 
0.2%
ad_23000000000_2_5_1 1
 
0.2%
ad_23000000000_2_6_1 1
 
0.2%
ad_23000000000_2_7_1 1
 
0.2%
Other values (572) 572
98.3%
2025-04-12T16:56:36.162028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5405
45.2%
_ 2328
19.5%
1 1231
 
10.3%
a 582
 
4.9%
d 582
 
4.9%
3 569
 
4.8%
2 551
 
4.6%
4 183
 
1.5%
5 118
 
1.0%
8 114
 
1.0%
Other values (3) 282
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5405
45.2%
_ 2328
19.5%
1 1231
 
10.3%
a 582
 
4.9%
d 582
 
4.9%
3 569
 
4.8%
2 551
 
4.6%
4 183
 
1.5%
5 118
 
1.0%
8 114
 
1.0%
Other values (3) 282
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5405
45.2%
_ 2328
19.5%
1 1231
 
10.3%
a 582
 
4.9%
d 582
 
4.9%
3 569
 
4.8%
2 551
 
4.6%
4 183
 
1.5%
5 118
 
1.0%
8 114
 
1.0%
Other values (3) 282
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5405
45.2%
_ 2328
19.5%
1 1231
 
10.3%
a 582
 
4.9%
d 582
 
4.9%
3 569
 
4.8%
2 551
 
4.6%
4 183
 
1.5%
5 118
 
1.0%
8 114
 
1.0%
Other values (3) 282
 
2.4%
Distinct578
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size50.7 KiB
2025-04-12T16:56:36.311050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters18624
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique574 ?
Unique (%)98.6%

Sample

1st rowv0911dg40001cbta3ebc77u7vbp6b1gg
2nd rowv10033g50000caldhr3c77ub7mthrn5g
3rd rowv10033g50000cfgfgl3c77u9fehpnd8g
4th rowv10033g50000cuqnqlnog65qepkj72vg
5th rowv10033g50000cfl0uujc77u563dfii20
ValueCountFrequency (%)
v10033g50000cbbhn7rc77ufs59uo1k0 2
 
0.3%
v10033g50000cshphmvog65qg2rt78h0 2
 
0.3%
v12044gd0000c7v2pfbc77u5s8dm3gm0 2
 
0.3%
v10033g50000cbbhn7rc77u645irjja0 2
 
0.3%
v10033g50000cbdfo43c77u67cklnp5g 1
 
0.2%
v10033g50000cf8l1irc77u7amn6erug 1
 
0.2%
v10033g50000cfgfgl3c77u9fehpnd8g 1
 
0.2%
v10033g50000cuqnqlnog65qepkj72vg 1
 
0.2%
v10033g50000cfl0uujc77u563dfii20 1
 
0.2%
v0911dg40001ccb8iurc77u9qur48j70 1
 
0.2%
Other values (568) 568
97.6%
2025-04-12T16:56:36.517820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3879
20.8%
g 1374
 
7.4%
3 1215
 
6.5%
c 1135
 
6.1%
5 960
 
5.2%
7 911
 
4.9%
v 910
 
4.9%
1 798
 
4.3%
u 559
 
3.0%
o 522
 
2.8%
Other values (22) 6361
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3879
20.8%
g 1374
 
7.4%
3 1215
 
6.5%
c 1135
 
6.1%
5 960
 
5.2%
7 911
 
4.9%
v 910
 
4.9%
1 798
 
4.3%
u 559
 
3.0%
o 522
 
2.8%
Other values (22) 6361
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3879
20.8%
g 1374
 
7.4%
3 1215
 
6.5%
c 1135
 
6.1%
5 960
 
5.2%
7 911
 
4.9%
v 910
 
4.9%
1 798
 
4.3%
u 559
 
3.0%
o 522
 
2.8%
Other values (22) 6361
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3879
20.8%
g 1374
 
7.4%
3 1215
 
6.5%
c 1135
 
6.1%
5 960
 
5.2%
7 911
 
4.9%
v 910
 
4.9%
1 798
 
4.3%
u 559
 
3.0%
o 522
 
2.8%
Other values (22) 6361
34.2%

video_info_duration
Real number (ℝ)

Distinct511
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.637404
Minimum5.388
Maximum451.118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2025-04-12T16:56:36.605957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.388
5-th percentile7.6008
Q112.63775
median19.3425
Q330.83025
95-th percentile59.9711
Maximum451.118
Range445.73
Interquartile range (IQR)18.1925

Descriptive statistics

Standard deviation30.706283
Coefficient of variation (CV)1.1527506
Kurtosis78.399878
Mean26.637404
Median Absolute Deviation (MAD)8.4525
Skewness7.2441664
Sum15502.969
Variance942.87585
MonotonicityNot monotonic
2025-04-12T16:56:36.687441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.016 10
 
1.7%
14.885 4
 
0.7%
27.414 4
 
0.7%
10.967 3
 
0.5%
16.32 3
 
0.5%
9.536 3
 
0.5%
10.008 3
 
0.5%
9.08 3
 
0.5%
30 3
 
0.5%
11.534 2
 
0.3%
Other values (501) 544
93.5%
ValueCountFrequency (%)
5.388 2
0.3%
5.48 1
0.2%
5.551 1
0.2%
5.573 1
0.2%
5.634 1
0.2%
5.641 2
0.3%
5.967 2
0.3%
6.016 1
0.2%
6.08 1
0.2%
6.222 1
0.2%
ValueCountFrequency (%)
451.118 1
0.2%
271.301 1
0.2%
257.672 1
0.2%
231.665 1
0.2%
144.706 1
0.2%
144.5 1
0.2%
139.534 1
0.2%
129.677 1
0.2%
113.707 1
0.2%
109.085 1
0.2%

video_info_cover
URL

Unique 

Distinct582
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size149.0 KiB
https://p16-sign-va.tiktokcdn.com/tos-maliva-v-2c3654-us/494db6fdb0ea4906be409b526785cb53~tplv-noop.image?x-expires=1743821622&x-signature=dZFSER844mwNCnmfGh9V8DPBHjI%3D
 
1
https://p16-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cm1q3nnog65iu5pn1020/1575517216/367~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746394572&x-signature=deSb7%2BkOzXpRamsXtUKHnHb96hM%3D
 
1
https://p16-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/o8A7DzcAoHfiBcim24IBcjAoTnwvXl6bhRrE6B~tplv-noop.image?x-expires=1743824324&x-signature=ajiL8xR0fQN%2Bhcd8wD1MSfP0TqQ%3D
 
1
https://p16-sign-va.tiktokcdn.com/tos-maliva-p-0068c799-us/ba9d3326dc1644ac80369686117d3612_1667672982~tplv-noop.image?x-expires=1743824354&x-signature=5IV0fIHTAocNx2p2XYIlbh3DF2Q%3D
 
1
https://p16-sign-va.tiktokcdn.com/tos-maliva-p-0068c799-us/okwCVEfEsAgundiVHQBWR1AjWlI8FuEeD9Su5C~tplv-noop.image?x-expires=1743824381&x-signature=GTk%2BwlWoObaXo8ITYZ9%2BXRk9ZMs%3D
 
1
Other values (577)
577 
ValueCountFrequency (%)
https://p16-sign-va.tiktokcdn.com/tos-maliva-v-2c3654-us/494db6fdb0ea4906be409b526785cb53~tplv-noop.image?x-expires=1743821622&x-signature=dZFSER844mwNCnmfGh9V8DPBHjI%3D 1
 
0.2%
https://p16-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cm1q3nnog65iu5pn1020/1575517216/367~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746394572&x-signature=deSb7%2BkOzXpRamsXtUKHnHb96hM%3D 1
 
0.2%
https://p16-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/o8A7DzcAoHfiBcim24IBcjAoTnwvXl6bhRrE6B~tplv-noop.image?x-expires=1743824324&x-signature=ajiL8xR0fQN%2Bhcd8wD1MSfP0TqQ%3D 1
 
0.2%
https://p16-sign-va.tiktokcdn.com/tos-maliva-p-0068c799-us/ba9d3326dc1644ac80369686117d3612_1667672982~tplv-noop.image?x-expires=1743824354&x-signature=5IV0fIHTAocNx2p2XYIlbh3DF2Q%3D 1
 
0.2%
https://p16-sign-va.tiktokcdn.com/tos-maliva-p-0068c799-us/okwCVEfEsAgundiVHQBWR1AjWlI8FuEeD9Su5C~tplv-noop.image?x-expires=1743824381&x-signature=GTk%2BwlWoObaXo8ITYZ9%2BXRk9ZMs%3D 1
 
0.2%
https://p16-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000ceugsd3c77u1e2cjcia0/1575517216/265~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746394711&x-signature=y9AoZozfIfEUGMEKEFC8bKHHTiQ%3D 1
 
0.2%
https://p19-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cna9nrvog65tn5at1aug/1575517216/1060~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746394711&x-signature=ewJAAsXpr2WQ5GxnjGdRgP98CT0%3D 1
 
0.2%
https://p16-sign-va.tiktokcdn.com/tos-maliva-p-0068c799-us/oEvZDfnzCEJSQftpSBngFBCKIEVVAKdS7DlbGy~tplv-noop.image?x-expires=1743824342&x-signature=jgHU6CJVtX616l2aXzCbZ%2Fl2Kis%3D 1
 
0.2%
https://p16-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cfmjsdrc77u02p73au6g/1575517216/832~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746394711&x-signature=mo8gK8Z8vFvnldtIu6JPRWxXg6A%3D 1
 
0.2%
https://p16-sign-va.tiktokcdn.com/tos-maliva-p-0068c799-us/oQEUPxREEDAf3IxKAnlgVgKLSBFNfcSDnHUz2j~tplv-noop.image?x-expires=1743824322&x-signature=SkI6y9RhyiFHuLkV2jas8%2BClUCI%3D 1
 
0.2%
Other values (572) 572
98.3%
ValueCountFrequency (%)
https 582
100.0%
ValueCountFrequency (%)
p19-cc-sign-sg.tiktokcdn.com 218
37.5%
p16-cc-sign-sg.tiktokcdn.com 216
37.1%
p16-sign-va.tiktokcdn.com 122
21.0%
p16-sign-sg.tiktokcdn.com 15
 
2.6%
p16-vod-sign-useast2a.tiktokcdn-eu.com 8
 
1.4%
p19-vod-sign-useast2a.tiktokcdn-eu.com 3
 
0.5%
ValueCountFrequency (%)
/tos-alisg-p-0051c001-sg/v10033g50000cbbhn7rc77ufs59uo1k0/1575517216/166~tplv-yenboaefse-image.jpeg 2
 
0.3%
/tos-maliva-p-0068c799-us/70f8e328a7024697b593af0d8c7fbd1c_1644047728~tplv-noop.image 2
 
0.3%
/tos-alisg-p-0051c001-sg/v10033g50000cbbhn7rc77u645irjja0/1575517216/358~tplv-yenboaefse-image.jpeg 2
 
0.3%
/tos-alisg-p-0051c001-sg/v10033g50000cshphmvog65qg2rt78h0/1575517216/815~tplv-yenboaefse-image.jpeg 2
 
0.3%
/tos-maliva-v-2c3654-us/494db6fdb0ea4906be409b526785cb53~tplv-noop.image 1
 
0.2%
/tos-maliva-p-0068c799-us/oEvZDfnzCEJSQftpSBngFBCKIEVVAKdS7DlbGy~tplv-noop.image 1
 
0.2%
/tos-maliva-p-0068c799-us/ba9d3326dc1644ac80369686117d3612_1667672982~tplv-noop.image 1
 
0.2%
/tos-maliva-p-0068c799-us/okwCVEfEsAgundiVHQBWR1AjWlI8FuEeD9Su5C~tplv-noop.image 1
 
0.2%
/tos-alisg-p-0051c001-sg/v10033g50000ceugsd3c77u1e2cjcia0/1575517216/265~tplv-yenboaefse-image.jpeg 1
 
0.2%
/tos-alisg-p-0051c001-sg/v10033g50000cna9nrvog65tn5at1aug/1575517216/1060~tplv-yenboaefse-image.jpeg 1
 
0.2%
Other values (568) 568
97.6%
ValueCountFrequency (%)
x-expires=1743821622&x-signature=dZFSER844mwNCnmfGh9V8DPBHjI%3D 1
 
0.2%
lk3s=317596d8&x-expires=1746394572&x-signature=deSb7%2BkOzXpRamsXtUKHnHb96hM%3D 1
 
0.2%
x-expires=1743824324&x-signature=ajiL8xR0fQN%2Bhcd8wD1MSfP0TqQ%3D 1
 
0.2%
x-expires=1743824354&x-signature=5IV0fIHTAocNx2p2XYIlbh3DF2Q%3D 1
 
0.2%
x-expires=1743824381&x-signature=GTk%2BwlWoObaXo8ITYZ9%2BXRk9ZMs%3D 1
 
0.2%
lk3s=317596d8&x-expires=1746394711&x-signature=y9AoZozfIfEUGMEKEFC8bKHHTiQ%3D 1
 
0.2%
lk3s=317596d8&x-expires=1746394711&x-signature=ewJAAsXpr2WQ5GxnjGdRgP98CT0%3D 1
 
0.2%
x-expires=1743824342&x-signature=jgHU6CJVtX616l2aXzCbZ%2Fl2Kis%3D 1
 
0.2%
lk3s=317596d8&x-expires=1746394711&x-signature=mo8gK8Z8vFvnldtIu6JPRWxXg6A%3D 1
 
0.2%
x-expires=1743824322&x-signature=SkI6y9RhyiFHuLkV2jas8%2BClUCI%3D 1
 
0.2%
Other values (572) 572
98.3%
ValueCountFrequency (%)
582
100.0%

video_info_width
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean602.61856
Minimum540
Maximum1280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2025-04-12T16:56:36.750024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum540
5-th percentile540
Q1576
median576
Q3576
95-th percentile720
Maximum1280
Range740
Interquartile range (IQR)0

Descriptive statistics

Standard deviation75.553099
Coefficient of variation (CV)0.12537466
Kurtosis25.568847
Mean602.61856
Median Absolute Deviation (MAD)0
Skewness3.9573449
Sum350724
Variance5708.2708
MonotonicityNot monotonic
2025-04-12T16:56:36.918323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
576 429
73.7%
720 100
 
17.2%
540 47
 
8.1%
1024 3
 
0.5%
1280 2
 
0.3%
608 1
 
0.2%
ValueCountFrequency (%)
540 47
 
8.1%
576 429
73.7%
608 1
 
0.2%
720 100
 
17.2%
1024 3
 
0.5%
1280 2
 
0.3%
ValueCountFrequency (%)
1280 2
 
0.3%
1024 3
 
0.5%
720 100
 
17.2%
608 1
 
0.2%
576 429
73.7%
540 47
 
8.1%

video_info_height
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1054.7216
Minimum572
Maximum1290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2025-04-12T16:56:36.971333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum572
5-th percentile960
Q11024
median1024
Q31024
95-th percentile1280
Maximum1290
Range718
Interquartile range (IQR)0

Descriptive statistics

Standard deviation118.1387
Coefficient of variation (CV)0.11200936
Kurtosis3.4339768
Mean1054.7216
Median Absolute Deviation (MAD)0
Skewness0.088797173
Sum613848
Variance13956.752
MonotonicityNot monotonic
2025-04-12T16:56:37.029245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1024 414
71.1%
1280 97
 
16.7%
960 46
 
7.9%
576 7
 
1.2%
720 3
 
0.5%
1026 2
 
0.3%
1022 2
 
0.3%
1276 1
 
0.2%
572 1
 
0.2%
1032 1
 
0.2%
Other values (8) 8
 
1.4%
ValueCountFrequency (%)
572 1
 
0.2%
576 7
 
1.2%
720 3
 
0.5%
844 1
 
0.2%
956 1
 
0.2%
960 46
 
7.9%
986 1
 
0.2%
992 1
 
0.2%
1022 2
 
0.3%
1024 414
71.1%
ValueCountFrequency (%)
1290 1
 
0.2%
1280 97
 
16.7%
1276 1
 
0.2%
1246 1
 
0.2%
1080 1
 
0.2%
1032 1
 
0.2%
1030 1
 
0.2%
1026 2
 
0.3%
1024 414
71.1%
1022 2
 
0.3%
Distinct115
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size42.4 KiB
2025-04-12T16:56:37.173728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length46
Median length26
Mean length17.407216
Min length3

Characters and Unicode

Total characters10131
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)4.8%

Sample

1st rowGames & Utility Software
2nd rowGames & Utility Software
3rd rowGames & Utility Software
4th rowGames & Utility Software
5th rowGames & Utility Software
ValueCountFrequency (%)
204
 
14.2%
services 31
 
2.2%
sports 31
 
2.2%
equipment 31
 
2.2%
platforms 28
 
1.9%
e-commerce 28
 
1.9%
clothing 26
 
1.8%
large 25
 
1.7%
care 24
 
1.7%
appliances 21
 
1.5%
Other values (159) 987
68.7%
2025-04-12T16:56:37.401504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 993
 
9.8%
854
 
8.4%
i 718
 
7.1%
r 647
 
6.4%
o 647
 
6.4%
s 600
 
5.9%
t 562
 
5.5%
a 547
 
5.4%
n 480
 
4.7%
c 398
 
3.9%
Other values (43) 3685
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10131
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 993
 
9.8%
854
 
8.4%
i 718
 
7.1%
r 647
 
6.4%
o 647
 
6.4%
s 600
 
5.9%
t 562
 
5.5%
a 547
 
5.4%
n 480
 
4.7%
c 398
 
3.9%
Other values (43) 3685
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10131
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 993
 
9.8%
854
 
8.4%
i 718
 
7.1%
r 647
 
6.4%
o 647
 
6.4%
s 600
 
5.9%
t 562
 
5.5%
a 547
 
5.4%
n 480
 
4.7%
c 398
 
3.9%
Other values (43) 3685
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10131
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 993
 
9.8%
854
 
8.4%
i 718
 
7.1%
r 647
 
6.4%
o 647
 
6.4%
s 600
 
5.9%
t 562
 
5.5%
a 547
 
5.4%
n 480
 
4.7%
c 398
 
3.9%
Other values (43) 3685
36.4%

industry_parent.value
Categorical

High correlation 

Distinct21
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size40.8 KiB
News & Entertainment
42 
Apps
42 
Games
40 
Sports & Outdoor
 
38
Beauty & Personal Care
 
38
Other values (16)
382 

Length

Max length24
Median length18
Mean length14.522337
Min length4

Characters and Unicode

Total characters8452
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNews & Entertainment
2nd rowNews & Entertainment
3rd rowNews & Entertainment
4th rowNews & Entertainment
5th rowNews & Entertainment

Common Values

ValueCountFrequency (%)
News & Entertainment 42
 
7.2%
Apps 42
 
7.2%
Games 40
 
6.9%
Sports & Outdoor 38
 
6.5%
Beauty & Personal Care 38
 
6.5%
Business Services 36
 
6.2%
Apparel & Accessories 31
 
5.3%
E-Commerce (Non-app) 28
 
4.8%
Food & Beverage 27
 
4.6%
Household Products 25
 
4.3%
Other values (11) 235
40.4%

Length

2025-04-12T16:56:37.487764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
242
 
18.9%
services 79
 
6.2%
apps 42
 
3.3%
news 42
 
3.3%
entertainment 42
 
3.3%
games 40
 
3.1%
sports 38
 
3.0%
outdoor 38
 
3.0%
beauty 38
 
3.0%
personal 38
 
3.0%
Other values (26) 639
50.0%

Most occurring characters

ValueCountFrequency (%)
e 1000
 
11.8%
696
 
8.2%
s 637
 
7.5%
r 541
 
6.4%
a 498
 
5.9%
o 492
 
5.8%
t 459
 
5.4%
n 420
 
5.0%
i 405
 
4.8%
c 348
 
4.1%
Other values (34) 2956
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1000
 
11.8%
696
 
8.2%
s 637
 
7.5%
r 541
 
6.4%
a 498
 
5.9%
o 492
 
5.8%
t 459
 
5.4%
n 420
 
5.0%
i 405
 
4.8%
c 348
 
4.1%
Other values (34) 2956
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1000
 
11.8%
696
 
8.2%
s 637
 
7.5%
r 541
 
6.4%
a 498
 
5.9%
o 492
 
5.8%
t 459
 
5.4%
n 420
 
5.0%
i 405
 
4.8%
c 348
 
4.1%
Other values (34) 2956
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1000
 
11.8%
696
 
8.2%
s 637
 
7.5%
r 541
 
6.4%
a 498
 
5.9%
o 492
 
5.8%
t 459
 
5.4%
n 420
 
5.0%
i 405
 
4.8%
c 348
 
4.1%
Other values (34) 2956
35.0%

objective_id
Categorical

High correlation 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size33.1 KiB
3
415 
2
116 
14
51 

Length

Max length2
Median length1
Mean length1.0876289
Min length1

Characters and Unicode

Total characters633
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 415
71.3%
2 116
 
19.9%
14 51
 
8.8%

Length

2025-04-12T16:56:37.552687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T16:56:37.592444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 415
71.3%
2 116
 
19.9%
14 51
 
8.8%

Most occurring characters

ValueCountFrequency (%)
3 415
65.6%
2 116
 
18.3%
1 51
 
8.1%
4 51
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 633
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 415
65.6%
2 116
 
18.3%
1 51
 
8.1%
4 51
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 633
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 415
65.6%
2 116
 
18.3%
1 51
 
8.1%
4 51
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 633
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 415
65.6%
2 116
 
18.3%
1 51
 
8.1%
4 51
 
8.1%

objective_value
Categorical

High correlation 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
Conversions
415 
App Installs
116 
Product sales
51 

Length

Max length13
Median length11
Mean length11.37457
Min length11

Characters and Unicode

Total characters6620
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApp Installs
2nd rowApp Installs
3rd rowApp Installs
4th rowApp Installs
5th rowApp Installs

Common Values

ValueCountFrequency (%)
Conversions 415
71.3%
App Installs 116
 
19.9%
Product sales 51
 
8.8%

Length

2025-04-12T16:56:37.648994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T16:56:37.692400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
conversions 415
55.4%
app 116
 
15.5%
installs 116
 
15.5%
product 51
 
6.8%
sales 51
 
6.8%

Most occurring characters

ValueCountFrequency (%)
s 1164
17.6%
n 946
14.3%
o 881
13.3%
e 466
7.0%
r 466
7.0%
C 415
 
6.3%
v 415
 
6.3%
i 415
 
6.3%
l 283
 
4.3%
p 232
 
3.5%
Other values (9) 937
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 1164
17.6%
n 946
14.3%
o 881
13.3%
e 466
7.0%
r 466
7.0%
C 415
 
6.3%
v 415
 
6.3%
i 415
 
6.3%
l 283
 
4.3%
p 232
 
3.5%
Other values (9) 937
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 1164
17.6%
n 946
14.3%
o 881
13.3%
e 466
7.0%
r 466
7.0%
C 415
 
6.3%
v 415
 
6.3%
i 415
 
6.3%
l 283
 
4.3%
p 232
 
3.5%
Other values (9) 937
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 1164
17.6%
n 946
14.3%
o 881
13.3%
e 466
7.0%
r 466
7.0%
C 415
 
6.3%
v 415
 
6.3%
i 415
 
6.3%
l 283
 
4.3%
p 232
 
3.5%
Other values (9) 937
14.2%

creative_features_creative_theme
Categorical

High correlation 

Distinct9
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
Product-Centric
221 
Promotional & Offer-Based
129 
Educational & Explainer
58 
Lifestyle & Aspirational
49 
Testimonial & Social Proof
37 
Other values (4)
88 

Length

Max length28
Median length26
Mean length20.350515
Min length14

Characters and Unicode

Total characters11844
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPromotional & Offer-Based
2nd rowProduct-Centric
3rd rowEducational & Explainer
4th rowLifestyle & Aspirational
5th rowProduct-Centric

Common Values

ValueCountFrequency (%)
Product-Centric 221
38.0%
Promotional & Offer-Based 129
22.2%
Educational & Explainer 58
 
10.0%
Lifestyle & Aspirational 49
 
8.4%
Testimonial & Social Proof 37
 
6.4%
Humor & Entertainment 32
 
5.5%
Not Applicable 24
 
4.1%
Brand Story & Mission-Driven 20
 
3.4%
Trend-Based & Reactive 12
 
2.1%

Length

2025-04-12T16:56:37.753942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T16:56:37.812868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
337
25.2%
product-centric 221
16.5%
offer-based 129
 
9.6%
promotional 129
 
9.6%
educational 58
 
4.3%
explainer 58
 
4.3%
lifestyle 49
 
3.7%
aspirational 49
 
3.7%
social 37
 
2.8%
proof 37
 
2.8%
Other values (10) 233
17.4%

Most occurring characters

ValueCountFrequency (%)
r 980
 
8.3%
o 959
 
8.1%
t 916
 
7.7%
i 852
 
7.2%
e 828
 
7.0%
755
 
6.4%
n 720
 
6.1%
a 704
 
5.9%
c 573
 
4.8%
l 465
 
3.9%
Other values (26) 4092
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 980
 
8.3%
o 959
 
8.1%
t 916
 
7.7%
i 852
 
7.2%
e 828
 
7.0%
755
 
6.4%
n 720
 
6.1%
a 704
 
5.9%
c 573
 
4.8%
l 465
 
3.9%
Other values (26) 4092
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 980
 
8.3%
o 959
 
8.1%
t 916
 
7.7%
i 852
 
7.2%
e 828
 
7.0%
755
 
6.4%
n 720
 
6.1%
a 704
 
5.9%
c 573
 
4.8%
l 465
 
3.9%
Other values (26) 4092
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 980
 
8.3%
o 959
 
8.1%
t 916
 
7.7%
i 852
 
7.2%
e 828
 
7.0%
755
 
6.4%
n 720
 
6.1%
a 704
 
5.9%
c 573
 
4.8%
l 465
 
3.9%
Other values (26) 4092
34.5%

creative_features_creative_concept
Categorical

High correlation 

Distinct21
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
Product demo
253 
None
98 
Limited-time offer
82 
Day-in-the-life story
36 
Before-and-after story
 
24
Other values (16)
89 

Length

Max length34
Median length28
Mean length12.498282
Min length3

Characters and Unicode

Total characters7274
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProduct demo
2nd rowProduct demo
3rd rowProduct demo
4th rowBefore-and-after story
5th rowProduct demo

Common Values

ValueCountFrequency (%)
Product demo 253
43.5%
None 98
 
16.8%
Limited-time offer 82
 
14.1%
Day-in-the-life story 36
 
6.2%
Before-and-after story 24
 
4.1%
FAQ 14
 
2.4%
Unboxing 13
 
2.2%
Cinematic brand film 7
 
1.2%
Meme-based content 7
 
1.2%
Behind-the-scenes 7
 
1.2%
Other values (11) 41
 
7.0%

Length

2025-04-12T16:56:37.906522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
product 253
24.6%
demo 253
24.6%
none 98
 
9.5%
limited-time 82
 
8.0%
offer 82
 
8.0%
story 64
 
6.2%
day-in-the-life 36
 
3.5%
before-and-after 24
 
2.3%
faq 14
 
1.4%
unboxing 13
 
1.3%
Other values (25) 110
10.7%

Most occurring characters

ValueCountFrequency (%)
e 869
11.9%
o 831
11.4%
d 652
 
9.0%
t 598
 
8.2%
r 503
 
6.9%
m 449
 
6.2%
447
 
6.1%
i 391
 
5.4%
c 284
 
3.9%
- 264
 
3.6%
Other values (29) 1986
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 869
11.9%
o 831
11.4%
d 652
 
9.0%
t 598
 
8.2%
r 503
 
6.9%
m 449
 
6.2%
447
 
6.1%
i 391
 
5.4%
c 284
 
3.9%
- 264
 
3.6%
Other values (29) 1986
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 869
11.9%
o 831
11.4%
d 652
 
9.0%
t 598
 
8.2%
r 503
 
6.9%
m 449
 
6.2%
447
 
6.1%
i 391
 
5.4%
c 284
 
3.9%
- 264
 
3.6%
Other values (29) 1986
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 869
11.9%
o 831
11.4%
d 652
 
9.0%
t 598
 
8.2%
r 503
 
6.9%
m 449
 
6.2%
447
 
6.1%
i 391
 
5.4%
c 284
 
3.9%
- 264
 
3.6%
Other values (29) 1986
27.3%
Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
Native Video
532 
Static Image
 
25
Animation & Motion Graphics
 
19
High-Production Video
 
3
Carousel
 
2

Length

Max length27
Median length12
Mean length12.534364
Min length8

Characters and Unicode

Total characters7295
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowNative Video
2nd rowNative Video
3rd rowAnimation & Motion Graphics
4th rowAnimation & Motion Graphics
5th rowAnimation & Motion Graphics

Common Values

ValueCountFrequency (%)
Native Video 532
91.4%
Static Image 25
 
4.3%
Animation & Motion Graphics 19
 
3.3%
High-Production Video 3
 
0.5%
Carousel 2
 
0.3%
Gamified Experience 1
 
0.2%

Length

2025-04-12T16:56:37.974109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T16:56:38.026066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
video 535
44.6%
native 532
44.3%
static 25
 
2.1%
image 25
 
2.1%
animation 19
 
1.6%
19
 
1.6%
motion 19
 
1.6%
graphics 19
 
1.6%
high-production 3
 
0.2%
carousel 2
 
0.2%
Other values (2) 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 1177
16.1%
e 1098
15.1%
a 623
8.5%
t 623
8.5%
618
8.5%
o 600
8.2%
d 539
7.4%
V 535
7.3%
N 532
7.3%
v 532
7.3%
Other values (23) 418
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1177
16.1%
e 1098
15.1%
a 623
8.5%
t 623
8.5%
618
8.5%
o 600
8.2%
d 539
7.4%
V 535
7.3%
N 532
7.3%
v 532
7.3%
Other values (23) 418
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1177
16.1%
e 1098
15.1%
a 623
8.5%
t 623
8.5%
618
8.5%
o 600
8.2%
d 539
7.4%
V 535
7.3%
N 532
7.3%
v 532
7.3%
Other values (23) 418
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1177
16.1%
e 1098
15.1%
a 623
8.5%
t 623
8.5%
618
8.5%
o 600
8.2%
d 539
7.4%
V 535
7.3%
N 532
7.3%
v 532
7.3%
Other values (23) 418
 
5.7%
Distinct8
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size37.0 KiB
None
229 
Customers
115 
Unclear
101 
Influencers
50 
Actors
44 
Other values (3)
43 

Length

Max length40
Median length35
Mean length7.7989691
Min length4

Characters and Unicode

Total characters4539
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInfluencers
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 229
39.3%
Customers 115
19.8%
Unclear 101
17.4%
Influencers 50
 
8.6%
Actors 44
 
7.6%
Combination of actors and customers 25
 
4.3%
Experts 16
 
2.7%
Combination of influencers and customers 2
 
0.3%

Length

2025-04-12T16:56:38.096139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T16:56:38.153273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
none 229
33.2%
customers 142
20.6%
unclear 101
14.6%
actors 69
 
10.0%
influencers 52
 
7.5%
combination 27
 
3.9%
of 27
 
3.9%
and 27
 
3.9%
experts 16
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e 592
13.0%
o 521
11.5%
n 515
11.3%
s 421
9.3%
r 380
 
8.4%
t 254
 
5.6%
c 249
 
5.5%
N 229
 
5.0%
u 194
 
4.3%
a 180
 
4.0%
Other values (14) 1004
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 592
13.0%
o 521
11.5%
n 515
11.3%
s 421
9.3%
r 380
 
8.4%
t 254
 
5.6%
c 249
 
5.5%
N 229
 
5.0%
u 194
 
4.3%
a 180
 
4.0%
Other values (14) 1004
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 592
13.0%
o 521
11.5%
n 515
11.3%
s 421
9.3%
r 380
 
8.4%
t 254
 
5.6%
c 249
 
5.5%
N 229
 
5.0%
u 194
 
4.3%
a 180
 
4.0%
Other values (14) 1004
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 592
13.0%
o 521
11.5%
n 515
11.3%
s 421
9.3%
r 380
 
8.4%
t 254
 
5.6%
c 249
 
5.5%
N 229
 
5.0%
u 194
 
4.3%
a 180
 
4.0%
Other values (14) 1004
22.1%
Distinct12
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
No People Featured
152 
Primarily Female
128 
Unclear
102 
Primarily Male
60 
Primarily Young Adults
50 
Other values (7)
90 

Length

Max length32
Median length28
Mean length16.242268
Min length7

Characters and Unicode

Total characters9453
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowPrimarily Male
2nd rowNo People Featured
3rd rowNo People Featured
4th rowPrimarily Female
5th rowNo People Featured

Common Values

ValueCountFrequency (%)
No People Featured 152
26.1%
Primarily Female 128
22.0%
Unclear 102
17.5%
Primarily Male 60
 
10.3%
Primarily Young Adults 50
 
8.6%
Diverse Age Range 43
 
7.4%
Diverse Gender Representation 24
 
4.1%
Diverse Ethnic Representation 10
 
1.7%
Primarily Middle-Aged Adults 5
 
0.9%
Primarily Asian 4
 
0.7%
Other values (2) 4
 
0.7%

Length

2025-04-12T16:56:38.230572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
primarily 251
18.6%
no 152
11.3%
featured 152
11.3%
people 152
11.3%
female 128
9.5%
unclear 102
7.6%
diverse 77
 
5.7%
male 60
 
4.4%
adults 58
 
4.3%
young 50
 
3.7%
Other values (10) 168
12.4%

Most occurring characters

ValueCountFrequency (%)
e 1430
15.1%
r 896
 
9.5%
a 777
 
8.2%
768
 
8.1%
l 760
 
8.0%
i 634
 
6.7%
P 403
 
4.3%
o 388
 
4.1%
m 380
 
4.0%
n 303
 
3.2%
Other values (26) 2714
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9453
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1430
15.1%
r 896
 
9.5%
a 777
 
8.2%
768
 
8.1%
l 760
 
8.0%
i 634
 
6.7%
P 403
 
4.3%
o 388
 
4.1%
m 380
 
4.0%
n 303
 
3.2%
Other values (26) 2714
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9453
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1430
15.1%
r 896
 
9.5%
a 777
 
8.2%
768
 
8.1%
l 760
 
8.0%
i 634
 
6.7%
P 403
 
4.3%
o 388
 
4.1%
m 380
 
4.0%
n 303
 
3.2%
Other values (26) 2714
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9453
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1430
15.1%
r 896
 
9.5%
a 777
 
8.2%
768
 
8.1%
l 760
 
8.0%
i 634
 
6.7%
P 403
 
4.3%
o 388
 
4.1%
m 380
 
4.0%
n 303
 
3.2%
Other values (26) 2714
28.7%
Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
Problem Aware
173 
Unclear
138 
Product Aware
112 
Solution Aware
112 
Unaware Audience
47 

Length

Max length16
Median length14
Mean length12.012027
Min length7

Characters and Unicode

Total characters6991
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnaware Audience
2nd rowUnaware Audience
3rd rowUnaware Audience
4th rowUnclear
5th rowUnaware Audience

Common Values

ValueCountFrequency (%)
Problem Aware 173
29.7%
Unclear 138
23.7%
Product Aware 112
19.2%
Solution Aware 112
19.2%
Unaware Audience 47
 
8.1%

Length

2025-04-12T16:56:38.288772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T16:56:38.334615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
aware 397
38.7%
problem 173
16.9%
unclear 138
 
13.5%
product 112
 
10.9%
solution 112
 
10.9%
unaware 47
 
4.6%
audience 47
 
4.6%

Most occurring characters

ValueCountFrequency (%)
r 867
12.4%
e 849
12.1%
a 629
 
9.0%
o 509
 
7.3%
w 444
 
6.4%
444
 
6.4%
A 444
 
6.4%
l 423
 
6.1%
n 344
 
4.9%
c 297
 
4.2%
Other values (9) 1741
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 867
12.4%
e 849
12.1%
a 629
 
9.0%
o 509
 
7.3%
w 444
 
6.4%
444
 
6.4%
A 444
 
6.4%
l 423
 
6.1%
n 344
 
4.9%
c 297
 
4.2%
Other values (9) 1741
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 867
12.4%
e 849
12.1%
a 629
 
9.0%
o 509
 
7.3%
w 444
 
6.4%
444
 
6.4%
A 444
 
6.4%
l 423
 
6.1%
n 344
 
4.9%
c 297
 
4.2%
Other values (9) 1741
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 867
12.4%
e 849
12.1%
a 629
 
9.0%
o 509
 
7.3%
w 444
 
6.4%
444
 
6.4%
A 444
 
6.4%
l 423
 
6.1%
n 344
 
4.9%
c 297
 
4.2%
Other values (9) 1741
24.9%
Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size36.9 KiB
Sales
259 
Awareness
115 
App Promotion
94 
Engagement
57 
Unclear
28 
Other values (2)
29 

Length

Max length13
Median length10
Mean length7.6924399
Min length5

Characters and Unicode

Total characters4477
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApp Promotion
2nd rowAwareness
3rd rowAwareness
4th rowEngagement
5th rowApp Promotion

Common Values

ValueCountFrequency (%)
Sales 259
44.5%
Awareness 115
19.8%
App Promotion 94
 
16.2%
Engagement 57
 
9.8%
Unclear 28
 
4.8%
Leads 22
 
3.8%
Traffic 7
 
1.2%

Length

2025-04-12T16:56:38.405499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-12T16:56:38.459484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sales 259
38.3%
awareness 115
17.0%
app 94
 
13.9%
promotion 94
 
13.9%
engagement 57
 
8.4%
unclear 28
 
4.1%
leads 22
 
3.3%
traffic 7
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 653
14.6%
s 511
11.4%
a 488
10.9%
n 351
 
7.8%
l 287
 
6.4%
o 282
 
6.3%
S 259
 
5.8%
r 244
 
5.5%
A 209
 
4.7%
p 188
 
4.2%
Other values (14) 1005
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 653
14.6%
s 511
11.4%
a 488
10.9%
n 351
 
7.8%
l 287
 
6.4%
o 282
 
6.3%
S 259
 
5.8%
r 244
 
5.5%
A 209
 
4.7%
p 188
 
4.2%
Other values (14) 1005
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 653
14.6%
s 511
11.4%
a 488
10.9%
n 351
 
7.8%
l 287
 
6.4%
o 282
 
6.3%
S 259
 
5.8%
r 244
 
5.5%
A 209
 
4.7%
p 188
 
4.2%
Other values (14) 1005
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 653
14.6%
s 511
11.4%
a 488
10.9%
n 351
 
7.8%
l 287
 
6.4%
o 282
 
6.3%
S 259
 
5.8%
r 244
 
5.5%
A 209
 
4.7%
p 188
 
4.2%
Other values (14) 1005
22.4%

Interactions

2025-04-12T16:56:31.307568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:12.514840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:14.522180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:25.592271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:27.508856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:29.344145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:31.370598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:12.585524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:16.051400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:25.649353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:27.565586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:29.405283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:32.994614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:14.288093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:18.992424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:27.273758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:29.111986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:31.056614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:33.056029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:14.343103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:20.579514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:27.328484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:29.168060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:31.117682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:33.116665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:14.398737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:22.237583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:27.386271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:29.223153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:31.177860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:33.181218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:14.459361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:23.930385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:27.447052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:29.282765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-12T16:56:31.243415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-12T16:56:38.529194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
costcreative_features_audience_focuscreative_features_campaign_objectivecreative_features_creative_conceptcreative_features_creative_themecreative_features_demographic_representationcreative_features_format_production_stylecreative_features_talent_typectridindustry_parent.valuelikeobjective_idobjective_valuescrap_datetimevideo_info_durationvideo_info_heightvideo_info_width
cost1.0000.1230.1230.0000.1060.0850.1080.0340.0000.0830.1400.0000.0580.0580.1680.0000.0000.000
creative_features_audience_focus0.1231.0000.3670.2780.3120.1530.1900.2260.0330.0710.3250.0180.2270.2270.3610.0850.0850.096
creative_features_campaign_objective0.1230.3671.0000.3260.4320.1560.1490.1900.0500.1020.3990.0000.4120.4120.4440.0370.0740.037
creative_features_creative_concept0.0000.2780.3261.0000.5580.0640.3340.1280.1730.1860.1740.0000.1700.1700.2000.0000.2000.010
creative_features_creative_theme0.1060.3120.4320.5581.0000.1690.1500.1890.0000.0280.2850.0000.2440.2440.3100.1390.0990.000
creative_features_demographic_representation0.0850.1530.1560.0640.1691.0000.0000.3920.0000.1380.1730.0000.1520.1520.1530.1140.0680.118
creative_features_format_production_style0.1080.1900.1490.3340.1500.0001.0000.0990.0000.0930.1610.0000.1620.1620.1950.0620.0670.186
creative_features_talent_type0.0340.2260.1900.1280.1890.3920.0991.0000.0000.1100.2290.0000.1520.1520.2340.1640.0000.000
ctr0.0000.0330.0500.1730.0000.0000.0000.0001.000-0.4290.1180.0550.3550.3550.5480.1420.0270.036
id0.0830.0710.1020.1860.0280.1380.0930.110-0.4291.0000.186-0.1210.0000.0000.000-0.0340.0960.072
industry_parent.value0.1400.3250.3990.1740.2850.1730.1610.2290.1180.1861.0000.0000.4160.4160.9770.0810.1470.056
like0.0000.0180.0000.0000.0000.0000.0000.0000.055-0.1210.0001.0000.0000.0000.000-0.0670.003-0.011
objective_id0.0580.2270.4120.1700.2440.1520.1620.1520.3550.0000.4160.0001.0001.0000.9610.1440.1000.000
objective_value0.0580.2270.4120.1700.2440.1520.1620.1520.3550.0000.4160.0001.0001.0000.9610.1440.1000.000
scrap_datetime0.1680.3610.4440.2000.3100.1530.1950.2340.5480.0000.9770.0000.9610.9611.0000.2140.0870.000
video_info_duration0.0000.0850.0370.0000.1390.1140.0620.1640.142-0.0340.081-0.0670.1440.1440.2141.0000.0490.095
video_info_height0.0000.0850.0740.2000.0990.0680.0670.0000.0270.0960.1470.0030.1000.1000.0870.0491.0000.864
video_info_width0.0000.0960.0370.0100.0000.1180.1860.0000.0360.0720.056-0.0110.0000.0000.0000.0950.8641.000

Missing values

2025-04-12T16:56:33.308582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-12T16:56:33.547744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ad_titlebrand_namecostctrfavoriteidis_searchliketagdetail_analysisscrap_datetimevideo_namevideo_info_vidvideo_info_durationvideo_info_covervideo_info_widthvideo_info_heightindustry_child.valueindustry_parent.valueobjective_idobjective_valuecreative_features_creative_themecreative_features_creative_conceptcreative_features_format_production_stylecreative_features_talent_typecreative_features_demographic_representationcreative_features_audience_focuscreative_features_campaign_objective
0Great time killer!Survival Game Master20.01False7132878852058906625True382133.0This ad is using Product Review to catch audience's attention and improve ads performance.\n\n'All right, I got them all ready. Let's see who wins. Oh, not looking good. Oh, no one want? I guess I get to keep all the money. With more money, I can actually get more contestants. And as you get more money, you can actually unlock more mini games like the. Like the glass bridge game later on down the line. And you can become the survival game masteralright? Let's go ahead. Throw them into the game. Who's gonna win this one? Who do you guys think? It's not looking good. Looks like i'm gonna. I'm gonna keep all the money again. Haha. Click here and download survival game master today.'\n\n1. Showcase: The advertisement demonstrates the game's excitement and unpredictability by showcasing the contestants' reactions and the outcome of the game. This can pique the target audience's interest and encourage them to download the game.\n2. Highlight Selling Points: The voiceover highlights the game's features, such as unlocking more mini-games and becoming the survival game master, which can entice potential players to try the game. Additionally, the text-over displays the game's high rewards, such as $300 and $8.1K, which can further motivate the target audience to download the game.\n2025-04-04T17:53:16.735723ad_23000000000_2_0_1v0911dg40001cbta3ebc77u7vbp6b1gg30.974https://p16-sign-va.tiktokcdn.com/tos-maliva-v-2c3654-us/494db6fdb0ea4906be409b526785cb53~tplv-noop.image?x-expires=1743821622&x-signature=dZFSER844mwNCnmfGh9V8DPBHjI%3D7201280Games & Utility SoftwareNews & Entertainment2App InstallsPromotional & Offer-BasedProduct demoNative VideoInfluencersPrimarily MaleUnaware AudienceApp Promotion
1Oddly satisfying gameGameworld Master20.01False7109275920046178305True142993.0This ad is using Strategy Focused to catch audience's attention and improve ads performance.\n\n1. Attention Grabber: The ad uses a variety of attention-grabbing tactics, such as bold text-overlay and abrupt voice-over interruptions, to capture viewers' attention and draw them into the advertisement.\n2. Highlight Selling Points: The voice-over mentions several selling points, including a 300/2 and 300/1 ratio, VIP, and a 200 price point, which are highlighted to differentiate the product from competitors and increase its appeal to potential buyers.\n2025-04-04T17:53:16.735723ad_23000000000_2_1_1v10033g50000caldhr3c77ub7mthrn5g37.334https://p16-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000caldhr3c77ub7mthrn5g/1575517216/719~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746391996&x-signature=PTGBMpJSUHP97IETyxR5NqxYi7s%3D5761024Games & Utility SoftwareNews & Entertainment2App InstallsProduct-CentricProduct demoNative VideoNoneNo People FeaturedUnaware AudienceAwareness
2My friend recommended me to play this gameSugarcane Inc. Empire Tycoon00.02False7077499601561305089True2427unknown2025-04-04T17:53:16.735723ad_23000000000_2_2_1v10033g50000cfgfgl3c77u9fehpnd8g17.323https://p19-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cfgfgl3c77u9fehpnd8g/1575517216/217~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746391996&x-signature=dzAgIECB3qQyC6WaS%2Foe%2FXlkCq0%3D5761024Games & Utility SoftwareNews & Entertainment2App InstallsEducational & ExplainerProduct demoAnimation & Motion GraphicsNoneNo People FeaturedUnaware AudienceAwareness
3download nowSmart VPN - Fast, Stable20.02False7176836203668783106True1247713.0This ad is using Oddly Satisfying to catch audience's attention and improve ads performance.\n\n1. Comment Reply: The ad creatively utilizes comment reply as a hook to grab the audience's attention. By incorporating a popular character from Frozen, the advertisement is able to connect with a wide audience, especially children and families.\n2. Respond to Comments: The ad effectively responds to comments made by the audience, showcasing the brand's engagement and interaction with its customers. This technique can increase customer loyalty and encourage more people to interact with the brand.\n3. Highlight Selling Points: The ad highlights the unique selling point of the product, which is the ability to undo paint strokes. This feature is emphasized through the use of the phrase "Undo Paint stroke", creating an impression that the product is innovative and user-friendly.\n2025-04-04T17:53:16.735723ad_23000000000_2_3_1v10033g50000cuqnqlnog65qepkj72vg53.015https://p19-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cuqnqlnog65qepkj72vg/1575517216/852~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746391996&x-signature=%2BUNX5vm4v0V0rWJDmD%2BP9TX81XI%3D7201280Games & Utility SoftwareNews & Entertainment2App InstallsLifestyle & AspirationalBefore-and-after storyAnimation & Motion GraphicsNonePrimarily FemaleUnclearEngagement
4Play ten minutes a day to relieve stress!Sugarcane Factory 3D00.02False7077499507373899777True2739unknown2025-04-04T17:53:16.735723ad_23000000000_2_4_1v10033g50000cfl0uujc77u563dfii2014.016https://p16-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cfl0uujc77u563dfii20/1575517216/375~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746391996&x-signature=QZDWtxw%2FFr%2Be8slvJATQCfipPBI%3D5761024Games & Utility SoftwareNews & Entertainment2App InstallsProduct-CentricProduct demoAnimation & Motion GraphicsNoneNo People FeaturedUnaware AudienceApp Promotion
5Stay social while social-distancing.LiveMe – Live Stream & Go Live10.02False7140228677053972482True5491unknownThis ad is using Product Review to catch audience's attention and improve ads performance.\n\n'Can you do my edges so I can get on live me? What is live me? Live you listen. Live me. Live me as an apple. We can go live, you know, get a lot of viewers and it be some fine dudes on here. Where? On who? On live me in. What is it? And hold on, hold on. Download this app. Download lives me so you can talk to girls like us.'\n\n1. Product Features: The ad highlights the app's capabilities of allowing users to talk to other people in real-time, and the speaker expresses their desire to use the app to improve their appearance.\n2. Product Review: The speaker provides a first-person account of their experience with the app, mentioning the potential for attracting a large audience and finding 'fine dudes' on the platform.\n3. Highlight Selling Points: The ad emphasizes the app's ability to allow users to talk to girls and the potential for attracting a large audience, which are likely to be appealing features to the target audience.\n2025-04-04T17:53:16.735723ad_23000000000_2_5_1v0911dg40001ccb8iurc77u9qur48j7019.318https://p16-sign-va.tiktokcdn.com/tos-maliva-v-2c3654-us/40cfa29197384e17bbe9a73579448b74~tplv-noop.image?x-expires=1743821611&x-signature=o5VJpfvG0tUHf7Jt4FgpC58AxMU%3D7201280Relationship InformationNews & Entertainment2App InstallsNot ApplicableNoneNative VideoCustomersPrimarily FemaleUnaware AudienceApp Promotion
6Swallow 'em allAttack Hole10.03False7228439319760535554True1811unknownThis ad is using Rhetorical Question to catch audience's attention and improve ads performance.\n\n'Hey, babe, how big is your hole? What? We're in public, bro. No, I mean your hole. In that hole. Oh, I need to get my hole bigger. Bigger and bigger. By collecting these objects before the time runs out. Till my hole can swallow everything. Can we be this big giant man? Download attack hole today.'\n\n1. Attention Grabber: The video's opening line, "Hey, babe, how big is your hole?", is an attention grabber that piques the viewer's interest and encourages them to continue watching. The use of the word "hole" is intentionally provocative and may elicit a reaction from some viewers.\n2. Highlight Selling Points: The ad highlights the objective of the game, which is to collect objects to enlarge a hole, and the potential for the player to become a "big giant man". This concept is likely to resonate with the target audience and motivate them to engage with the game.\n2025-04-04T17:53:16.735723ad_23000000000_2_6_1v0911dg40001ch8946nog65oubt50a7025.250https://p16-sign-va.tiktokcdn.com/tos-maliva-v-2c3654-us/2e6f83ab56ee481091d1f8568d697fcd~tplv-noop.image?x-expires=1743821617&x-signature=2L7R6hwmx6BXLO1p2aTXsmf9gq0%3D7201280Other News & EntertainmentNews & Entertainment2App InstallsHumor & EntertainmentProduct demoNative VideoCombination of actors and customersDiverse Gender RepresentationUnaware AudienceApp Promotion
7Check Out my new SUPERCAR!Car Games ·20.03False7123282891630772226True2681unknownThis ad is using Relatable Problem to catch audience's attention and improve ads performance.\n\n'Does anyone remember this game? This is what it's like now lmao.'\n\n1. Relatable Problem: The ad starts off by addressing a relatable problem, asking the audience if they remember a certain game. This immediately captures the audience's attention and creates a sense of nostalgia. By showing how the game has changed since then, it further emphasizes the relatability and allows the audience to connect with the ad.\n2. Highlight Selling Points: The ad also highlights the selling points of the game, such as the 'SCHOOL BUS' and 'bet you can't beat my drift' features, as well as the 'とうふ店' text overlay. These selling points help differentiate the game from its competitors and make it more appealing to potential players.\n2025-04-04T17:53:16.735723ad_23000000000_2_7_1v10033g50000cbdfo43c77u67cklnp5g12.179https://p16-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cbdfo43c77u67cklnp5g/1575517216/143~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746391996&x-signature=aZtOBaJVQEk4kMvcjLbpJmdJUso%3D5761024Games & Utility SoftwareNews & Entertainment2App InstallsHumor & EntertainmentComparisonNative VideoNoneNo People FeaturedProduct AwareEngagement
8Be My Guest is the new trendBe My Guest!20.04False7188113660753313794True2445unknownThis ad is using Product Demonstration to catch audience's attention and improve ads performance.\n\n'Hello? We want to rent a luxury house in italy for our honeymoon. Didn't you hear me? What the hell are you doing? Said italy. Fix it quickly! Call me your manager, quickly! It's all your fault!'\n\n1. Attention Grabber: The advertisement begins with a sudden and direct attention grabber, prompting the viewer to listen to the speaker's request.\n2. Problem & Solution: The speaker highlights a problem they face, which is not being able to rent a luxury house in Italy, and the solution they seek from the viewer, which is to fix the issue quickly.\n3. Highlight Selling Points: The ad emphasizes the importance of renting a luxury house in Italy for a honeymoon, which is perceived as a unique and desirable experience.\n2025-04-04T17:53:16.735723ad_23000000000_2_8_1v10033g50000cf8l1irc77u7amn6erug13.014https://p19-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cf8l1irc77u7amn6erug/1575517216/265~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746391996&x-signature=1SopkRheDz62Jz%2B1rSToV5czXik%3D5761024Other News & EntertainmentNews & Entertainment2App InstallsHumor & EntertainmentDay-in-the-life storyAnimation & Motion GraphicsNoneNo People FeaturedUnclearUnclear
9Play now or Cry laterBe My Guest20.04False7169928916990312449True8503unknownThis ad is using Problem & Solution to catch audience's attention and improve ads performance.\n\n'My tenants are very happy because I charge well below market prices and make them live in crappy houses. Then I buy more houses with the money I save from them. I'm very close to building a real estate empire.'\n\n1. Attention Grabber: The advertisement uses a controversial statement to grab the audience's attention, highlighting the disparity between the low rent charged and the poor living conditions. This technique may pique the curiosity of potential viewers and encourage them to continue watching the ad.\n2. Highlight Selling Points: The ad emphasizes the affordability of the properties and the opportunity to live in a desirable location for a lower price. The mention of the tenants being 'very happy' suggests that the landlord is providing value to their residents.\n2025-04-04T17:53:16.735723ad_23000000000_2_9_1v10033g50000cees7urc77ue9bgulc9030.016https://p19-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cees7urc77ue9bgulc90/1575517216/739~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746391996&x-signature=c15xXLvNy3ssgCdZ%2B%2FWEwYnaSwc%3D5761024Other News & EntertainmentNews & Entertainment2App InstallsHumor & EntertainmentDay-in-the-life storyNative VideoNoneNo People FeaturedUnclearApp Promotion
ad_titlebrand_namecostctrfavoriteidis_searchliketagdetail_analysisscrap_datetimevideo_namevideo_info_vidvideo_info_durationvideo_info_covervideo_info_widthvideo_info_heightindustry_child.valueindustry_parent.valueobjective_idobjective_valuecreative_features_creative_themecreative_features_creative_conceptcreative_features_format_production_stylecreative_features_talent_typecreative_features_demographic_representationcreative_features_audience_focuscreative_features_campaign_objective
572🧟‍♀️🖤💜💚 ZOMBIFIED 💚💜🖤🧟‍♀️ those NEON shades! #neonmakeup #uvmakeup #altmakeup # #makeup #halloweenmakeup #halloweenpalette #gothicmakeup10.38False7157344090140180481True3720unknown2025-04-04T19:02:02.674487ad_14000000000_14_17_1v09044g40000cd9ve4bc77ucufg1rmt026.307https://p16-sign-va.tiktokcdn.com/tos-maliva-p-0068/507bc72da21c4e428d6de14a851a0a55_1666447557~tplv-noop.image?x-expires=1743825744&x-signature=DHqMKXBSw%2BtIFiXsGuO5mVXWRNk%3D5761024CosmeticsBeauty & Personal Care14Product salesProduct-CentricProduct demoNative VideoActorsDiverse Gender RepresentationProduct AwareSales
573Monster KBB Tacos using our 2 Net Carb Tortillas! @ketohousewife #fyp #keto #kbbq #lowcarb10.01False7061134395470790658True62233.0This ad is using Product Demonstration to catch audience's attention and improve ads performance.\n\n'sudah bisa tersenyum.'\n\n1. Use Cases: The advertisement showcases a real-life use case of enjoying a meal with tortilla that only has 2 net carbs, which creates a connection with the target audience who are looking for low-carb options in their diet. This approach is effective in demonstrating the practicality of the product in everyday life.\n2. Product Demonstration: The product demonstration is concise and straightforward, with the text-over simply stating 'Tortilla only 2 net carbs btw'. This allows the audience to quickly grasp the main selling point of the product without any unnecessary distractions.\n3. Highlight Selling Points: The main selling point of the tortilla is highlighted effectively through the text-over, which states the product's low net carb count. This information is likely to appeal to health-conscious consumers who are looking for low-carb options in their diet.\n2025-04-04T19:04:10.315905ad_27000000000_14_0_1v12044gd0000c7v2pfbc77u5s8dm3gm014.465https://p16-sign-va.tiktokcdn.com/tos-maliva-p-0068c799-us/70f8e328a7024697b593af0d8c7fbd1c_1644047728~tplv-noop.image?x-expires=1743825864&x-signature=v0DssDniPfk9fnFqKpgxgirhkfM%3D5761024Food & Fresh ProduceFood & Beverage14Product salesProduct-CentricProduct demoNative VideoCustomersDiverse Age RangeSolution AwareAwareness
574Reach your ideal customers on TikTok. Get started on TikTok now!TikTok For Business20.18False6930891822495793154True672873.0This ad is using Product Demonstration to catch audience's attention and improve ads performance.\n\n1. Endorsements: There are no endorsements in the ad. No voice-over or text-over is used to promote any specific brand or product.\n2. Highlight Selling Points: The ad focuses on promoting TikTok as a platform for businesses, with the call-to-action to sign up now. The text-over displays the name of the platform and the call-to-action, emphasizing the selling point of being a platform specifically designed for businesses.\n2025-04-04T19:04:30.567421ad_24000000000_14_0_1v10033g50000cshphmvog65qg2rt78h016.320https://p19-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cshphmvog65qg2rt78h0/1575517216/815~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746396270&x-signature=v2bGTT9niCfWG%2B4rQ7BdGSN0UWU%3D5761024Marketing & AdvertisingBusiness Services14Product salesPromotional & Offer-BasedNoneNative VideoExpertsPrimarily MaleProblem AwareLeads
575Rev up your pet's playtime. The ultimate exercise toy for your furry friend.ActiveRollingBall20.08False7172740553150382081True31823.02025-04-04T19:04:57.200838ad_19000000000_14_0_1v12025gd0000cfnj383c77udvj9vdf7025.334https://p16-sign-va.tiktokcdn.com/tos-maliva-p-0068c799-us/cc3e0374c01a4d2dab0f5e3804d8c765~tplv-noop.image?x-expires=1743825922&x-signature=t6AulhVDp6uvN%2BuxYmjYRBZJ8FA%3D5761024Pet ToysPets14Product salesProduct-CentricProduct demoNative VideoNoneNo People FeaturedProblem AwareSales
576See why these are a favorite jean! 0-24W. All Judy Blue are amazinf but the tummy control jeans make you feel a bit more SNATCHED ✨ #judyblue #tummycontrol #jeans #tummycontroljeans #tryonhaul10.02False7211590884583194626True1842unknownThis ad is using Product Review to catch audience's attention and improve ads performance.\n\n'I'm a plus size girl, and we're about to try on some judy blue tummy control jeans. Let's. Let's check it out. I always get disbelievers. If this pair actually works. I mean, I would be a disbeliever, too. Unless I had it on my body. Yep. Like, totally works, right? When take a good, hard look at these. Okay. Like, yeah, I have a little pooch there still. But you haven't seen nothing yet. Okay? You have not seen anything yet. Let's take a good, hard look at this. Bring down. You can see all the goods there. Okay. Right. Y'all haven't seen anything yet. Let, let. Let's try this view. You guys haven't seen anything. You don't even know what's about to hit you. Do they work? Not at all. Guys, like, look at this. Like, I went from that to this. So for me, i'm a fourteen W in stores, I size down to the GD blue. Fifteen for pretty much every other pair of GD blue other than these. In the tummy control, I size one extra down. I am a size thirteen. And these guys. This is you guys. One more time. I can't believe i'm putting myself out on the internet like this. From this to. This, I mean, I think I would be a very bad friend if I kept this secret all to myself, and I did not share it with all the ladies. These jeans just make you look a little extra fab and amazing on them. Sure. Is my tummy completely gone? No, but only surgery can do that. This is all I got to do is put on a hot pair of jeans to make me feel a little bit more snatched. Um, hi. Sign me up.'\n\n1. Resonate with Target Audience: The speaker begins by addressing the target audience, emphasizing their shared experience as plus-size individuals and creating an instant connection.\n2. Product Review: The ad showcases the product's effectiveness and highlights its selling points, such as the tummy control feature and the fact that it makes the wearer look 'a little extra fab and amazing.'\n3. Highlight Selling Points: The voiceover emphasizes the benefits of the product, such as the tummy control feature and the fact that it makes the wearer look 'a little extra fab and amazing.' The text overlays also highlight the product's benefits.\n2025-04-04T19:05:13.964623ad_22000000000_14_0_1v12044gd0000cg8ic6bc77u4jk6f4sqg109.085https://p16-sign-va.tiktokcdn.com/tos-maliva-p-0068c799-us/3e41abd70f74462594532e46053bb0e5_1678845629~tplv-noop.image?x-expires=1743826021&x-signature=JXIGZDyje9G3ITcOBvQkFPpYO4A%3D7201280Women's ClothingApparel & Accessories14Product salesTestimonial & Social ProofBefore-and-after storyNative VideoCustomersPrimarily FemaleProblem AwareSales
577Let’s check out these Judy Blue pull on shorts! The perfect stretchy summer shirt does exist ☀️ #judyblue #shortoutfits #denimshorts #plussizeedition20.02False7208678574701985794True3435unknownThis ad is using Product Demonstration to catch audience's attention and improve ads performance.\n\n'The number one style of judy blue in our shop this week so far. It's only wednesday, is this pull on short. And I mean pull on. They are freaking amazing. They have like the perfect summerfabric. I love them. The back pockets are operational because they're like a chain. But real denim. Solook at this guys. You literally can just accept. I don't know how to step into them. You can just step into them and literally pull them up. They are freaking fantastic. Good, soft, stretchy. Amazing material on this. Look at that. Pull them on and golet's talk about these a little bit more. Other than the waist, judy blues, just good soft and stretchy on these. You canthey're just like perfect summer material. They come cuffed as you can see. You can uncuff them. So on a size medium, y'all, they in seems gonna be about a four inch cuffedyou can unroll it to a five inch and seam. And then for the plus size, it's gonna be about a five inch and seam unrolled to a seven. So I love that sometimes i'm wearing a longer shirt, like I had to pull this one up a little bit cause it was almost like covering my shorts, right? I could have just uncuffed my shorts or maybe I was likefeeling insecure that day, wanted to give my legs a little bit more coverage and could have done that as well. So let's take a look at this. This is it gonna be uncuffed one time on mejust a little bit longer and then you've got the two times. So look at that. And both of them got. Look at the stretch on these. They're freaking amazing. If you are not picking them up, they're not gonna be along foraround for long cause they are freaking amazing. Good soft, stretchy. If you haven't tried tik tok shops, there's gonna be a little butts in there kind of where you said the filter, but it's gonna say products or maybe it'll just say the duty blue shortsyou tap. That you can actually check out here on tik tok. We ship from vegas. If you have questions, ask me in the comments. I'll hit you back with some answers, y'all. But the one thing I can tell you is you need these. There are number one short this week right now for a reason.'\n\n1. Reviews/Popularity: The ad starts by highlighting the popularity of the product, stating it as the number one style in the shop that week. This strategy effectively leverages social proof and encourages viewers to take action.\n2. Product Review: The voiceover provides a positive review of the product, emphasizing its softness, stretchiness, and perfect summer fabric. This method helps build trust and credibility with potential customers.\n3. Product Demonstration: The host demonstrates the product by pulling them on and showing their features, such as the perfect fit and the chain detail on the back pocket. This helps viewers visualize themselves wearing the product and understand its quality.\n4. Highlight Selling Points: The ad highlights several selling points, including the material, fit, and versatility of the product. By emphasizing these key benefits, the ad effectively differentiates the product from competitors and persuades viewers to make a purchase.\n2025-04-04T19:05:13.964623ad_22000000000_14_1_1v12044gd0000cg0j68rc77ublve1g350129.677https://p16-sign-va.tiktokcdn.com/tos-maliva-p-0068c799-us/230305747c954469aa5455531a54a0a6_1677800499~tplv-noop.image?x-expires=1743826042&x-signature=x2NtW%2FaIuc6lYCOyQyHMew4u91U%3D7201280Women's ClothingApparel & Accessories14Product salesProduct-CentricProduct demoNative VideoUnclearPrimarily FemaleProduct AwareSales
578Back to school clothing haul W/ @calithekid_ - Free Shipping On All OrdersTwillMKT20.03False7138658492106047490True229383.0This ad is using Unboxing to catch audience's attention and improve ads performance.\n\n'Be unboxing all these clothes at twelve markets in ME. So without further ado, let's get into it. We'll be doing some styling videos with these in the future. First we got these flare pants right here. And some carpenter pants. Then we got these green cargo pants. Michael jordan is a GOAT. No debates. Kanye T, some slight just love yourself t shirt. Big shout out to twelve market for the clothes. Appreciate y'all.'\n\n1. Use Cases: The ad uses the hook of demonstrating the clothing items and the use cases for each item, creating a sense of anticipation and excitement for the viewers.\n2. Unboxing: The ad features an unboxing segment, showcasing the items in a visually engaging way and creating a sense of surprise and satisfaction.\n3. Highlight Selling Points: The ad highlights the selling points of the products, such as the Michael Jordan and Kanye West t-shirts, and emphasizes the quality and style of the items.\n2025-04-04T19:05:13.964623ad_22000000000_14_2_1v10033g50000chhvg5fog65ua00c6e6021.884https://p16-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/cc292815bf6e434892675690e80e5979~tplv-noop.image?x-expires=1743825935&x-signature=5aNRYHmW74aylEURzW7edsQ%2BzDE%3D5761024Men's ClothingApparel & Accessories14Product salesProduct-CentricUnboxingNative VideoInfluencersPrimarily Young AdultsProduct AwareSales
579You Sold These Out Last TimeJEM CITY20.13False7154668616717778946True71503.02025-04-04T19:05:13.964623ad_22000000000_14_3_1v10033g50000cga9vcbc77u9jqnibftg9.067https://p16-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cga9vcbc77u9jqnibftg/1575517216/166~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746396313&x-signature=YiL%2B4dQDHbXqXPwlhAIL0%2BB4agA%3D5761024Men's ClothingApparel & Accessories14Product salesProduct-CentricProduct demoNative VideoNoneNo People FeaturedUnclearSales
580Vintage Inspired Fleeces! Buy yours.JEM CITY20.14False7154671305471754241True44273.02025-04-04T19:05:13.964623ad_22000000000_14_4_1v10033g50000cg9f4hrc77u9jqni80og9.126https://p16-cc-sign-sg.tiktokcdn.com/tos-alisg-p-0051c001-sg/v10033g50000cg9f4hrc77u9jqni80og/1575517216/254~tplv-yenboaefse-image.jpeg?lk3s=317596d8&x-expires=1746396313&x-signature=9PXa9j%2B3QmSOVLVmFHpc4rw56wM%3D5761024Men's ClothingApparel & Accessories14Product salesProduct-CentricProduct demoNative VideoNoneNo People FeaturedUnclearAwareness
581Our new Micro Bags hold more than you think! 💁🏻‍♀️20.30False7221539646094213121True37782.02025-04-04T19:05:13.964623ad_22000000000_14_5_1v09044g40000cgs04b3c77u3uag45jv018.505https://p16-sign-va.tiktokcdn.com/tos-maliva-p-0068/oMER4kFE5BAhMQRUHCIAYeDBIAnZnOC75bestJ~tplv-noop.image?x-expires=1743825931&x-signature=mAqPek%2FjOWPSQnSLIuvn%2BfBTylU%3D5761024BagsApparel & Accessories14Product salesProduct-CentricProduct demoNative VideoNoneUnclearUnclearAwareness